The Investing “Dark Side”

Jul 2015 //
Risk vs. Volitility
History

Some people have convinced themselves that a lack of knowledge about economics and investing won’t stop them from earning good returns.  They believe, for example, that an investment’s risk is best understood not as the adverse possibilities driven by global competition, changing technology, unproductive corporate acquisitions, poor labor relations or any of a long list of other factors.  No (they say), risk is best understood as “beta” or some other measure of an investment’s day-to-day price changes.  We can illustrate the fallacy of this reasoning in just two sentences:  Consider a hypothetical investment that falls in price by the exact percentage decline of the S&P 500 on days the S&P 500 declines, and gains slightly more than the S&P 500 on days the S&P 500 gains.  By definition, this investment will have greater volatility than the S&P 500, but there is no reasonable way to regard it as riskier—unless you regard better gains as risky.  If you accept the notion that historical volatility (including upward volatility) is itself risky, you are ready to let a simple/simplistic computer program churn out “efficient” portfolios, provided you are ready to ignore history and accept an even shakier assumption—that the mathematical correlation between asset classes (stocks, bonds, etc.) and between individual investments within asset classes is knowable in advance.  (By the way, different computer models will invariably spit out different suggested portfolios, reflecting different human programming.)  Once you’ve essentially assumed away many of the realistic factors in investing, you can go buy any desired combination of large cap stocks, medium caps, small caps, international stocks, bonds, etc. for your portfolio.  Exchange traded funds (ETFs)—typically narrowly-defined “portfolios”—are made for this approach, and their popularity has increased in recent years.  However, since historical volatility and asset class correlations change all the time, computer models will frequently call for different allocations—the investing equivalent of a dog chasing its own tail.